Intelligent customization method for prediction model of longitudinal surface crack on continuously casting slab
YANG Li-an1, MI Jin-zhou2, LI Tao1, CONG Jun-qiang3, DENG Ai-jun3
1. Department of Electronic and Automation, Rizhao Steel Holding Group Co., Ltd., Rizhao 276800, Shandong, China; 2. Steel-making Engineering Division, China National Heavy Machinery Research Institue Co., Ltd., Xi′an 710032, Shanaxi, China; 3. School of Metallurgical Engineering, Anhui University of Technology, Ma′anshan 243002, Anhui, China
Abstract:In the process of steel products manufacturing, online prediction of the location of casting billet quality defects and timely offline cleaning of the defective billet are helpful to improve the production stability of continuous casting and rolling, and realize energy saving, emission reduction and green production of iron and steel enterprises.However, the continuous casting process has the characteristics of multivariable, time-varying and polymorphism. Only by combining the characteristics of equipment, steel grade and defects, and customizing the slab quality defect prediction model, can the slab quality defects be accurately predicted.Therefore, combining data communication technology, artificial intelligence technology, C# and Matlab hybrid programming technology, the intelligent online judgment system for continuous casting slab is established, the intelligent customization method of slab quality prediction model is studied, and the customization process is introduced by taking slab longitudinal crack prediction model as an example. The results show that this method can assist process engineers to intelligently customize the prediction model for steel grades and defects, reduce the difficulty of model development and optimization, and improve the reliability of slab quality prediction model.
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YANG Li-an, MI Jin-zhou, LI Tao, CONG Jun-qiang, DENG Ai-jun. Intelligent customization method for prediction model of longitudinal surface crack on continuously casting slab. CONTINUOUS CASTING, 2022, 41(6): 16-20.
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